Recently, companies have begun to explore the possibilities of using grid computing networks (grids) to increase the company's productivity. A grid comprises a plurality of computers that are networked together. Large or complex computations (jobs) can be broken up into a plurality of smaller, more manageable jobs by the grid. The smaller jobs are then sent out to the computers within the grid for parallel processing. As the individual computers complete their jobs, the grid reassembles the smaller jobs into the completed job. The end result is that the large, complex jobs are processed in significantly less time than is possible on a single computer.
One of the important components of a grid is the scheduler. The scheduler is an algorithm that decides how to distribute the individual job pieces for processing throughout the grid. Although the concept of a scheduler sounds simple, the decision-making process for distribution of the job pieces is extremely complex. A number of decisions must be made as to how the scheduler chooses one grid computer over another. The physical distance between computers, processing speed, available memory, cost of operating grid computers, queue for each computer, the topology of the network connectivity among the computers, special resources (i.e. hardware, software, or licenses) available on particular computers, and connectivity between hard drives are just a few of the factors taken into consideration in creating a scheduler. Logical factors, such as job operating characteristics, priorities of various kinds, operational constraints on the utilization of the grid system, and many others, must also be taken into consideration by the scheduler. Thus, there are a plurality of different schedulers that can be created to distribute the job pieces throughout the grid. Selecting the appropriate scheduler for a grid is made even more complex by the fact that the type of grid traffic changes depending on the time of day or the day of the week, month, or year. One scheduler may be more efficient in the mornings and another scheduler may be more efficient in the evenings. A third scheduler may be more efficient on the weekends or on the last day of every fiscal quarter. Thus, in order to operate a grid at maximum efficiency, a user will ideally change schedulers from time to time, depending on the operating conditions of the grid. It is difficult for a person to constantly analyze and change the schedulers, so an automated method is preferable. Currently, there is no automated method for dynamically or adaptively changing the selection of the schedulers to best use at a given time interval. Consequently, a need exists for an automated method for dynamically and adaptively changing the selection of a scheduler in a grid computing network.
One method for measuring the efficiency of the scheduler is to run a return on investment (ROI) calculator. An example of an ROI calculator is described in U.S. patent application Ser. No. 10/756,150 incorporated herein by reference. The ROI calculator can calculate, using simulation and other modeling methods, the return on investment of an IT infrastructure which employs a particular set of schedulers. The return on investment is a quantitative measure of how effectively the company's information technology (IT) infrastructure is implemented. The ROI calculator can also determine other properties associated with the grid such as operating efficiency, total operating cost, mean time to process individual jobs, and so forth. A user can run a ROI calculator for individual schedulers and operating conditions to determine which scheduler is best suited for which operating conditions. However, as schedulers are continuously modified and updated, an orderly method for applying the ROI calculator to the operating conditions and schedulers is needed. Therefore, what is needed is a method for determining how to select the operating conditions and schedulers using an ROI calculator as a measurement tool.
The Monte Carlo method for selecting criteria is well known in the art. The Monte Carlo method involves the random selection and application of criteria to a model. Proponents of the Monte Carlo method assert that the Monte Carlo method can be more efficient at finding near-optimum solutions than orderly search methods for particularly difficult problems. Schedulers and grid conditions, both being complex, are ideal for the Monte Carlo method. Therefore, what is needed is a method for applying the Monte Carlo method to schedulers and grid conditions for evaluation by an ROI calculator in order to determine the most efficient daily arrangement of schedulers to a grid.